论文标题
Distspectrl：在多代理增强学习系统中分发规格
DistSPECTRL: Distributing Specifications in Multi-Agent Reinforcement Learning Systems
论文作者
论文摘要
虽然在为一般网络物理系统指定和学习目标方面取得了显着进展，但将这些方法应用于分布式多代理系统仍带来重大挑战。其中包括（a）允许允许本地和全球目标表达和相互作用的工艺规范基础，（b）国家和行动空间中的驯服爆炸以实现有效的学习，以及（c）最大程度地减少协调频率以及参与参与者的全球目标。为了应对这些挑战，我们提出了一个新颖的规范框架，该框架允许自然组成用于指导多代理系统培训的本地和全球目标。我们的技术使学习表达性策略可以使代理人以无协调的方式为本地目标运作，同时使用分散的通信协议来强制执行全球。实验结果支持我们的主张，即使用规范指导的学习可以有效地实现复杂的多代理分布式计划问题。
While notable progress has been made in specifying and learning objectives for general cyber-physical systems, applying these methods to distributed multi-agent systems still pose significant challenges. Among these are the need to (a) craft specification primitives that allow expression and interplay of both local and global objectives, (b) tame explosion in the state and action spaces to enable effective learning, and (c) minimize coordination frequency and the set of engaged participants for global objectives. To address these challenges, we propose a novel specification framework that allows natural composition of local and global objectives used to guide training of a multi-agent system. Our technique enables learning expressive policies that allow agents to operate in a coordination-free manner for local objectives, while using a decentralized communication protocol for enforcing global ones. Experimental results support our claim that sophisticated multi-agent distributed planning problems can be effectively realized using specification-guided learning.
